Empirical comparison of density estimators for large carnivores

Abstract

Summary 1. Population density is a critical ecological parameter informing effective wildlife management and conservation decisions. Density is often estimated by dividing capture–recapture (C–R) estimates of abundance () by size of the study area, but this relies on the assumption of geographic closure – a situation rarely achieved in studies of large carnivores. For geographically open populations is overestimated relative to the size of the study area because animals with only part of their home range on the study area are available for capture. This bias (‘edge effect’) is more severe when animals such as large carnivores range widely. To compensate for edge effect, a boundary strip around the trap array is commonly included when estimating the effective trap area (). Various methods for estimating the width of the boundary strip are proposed, but / estimates of large carnivore density are generally mistrusted unless concurrent telemetry data are available to define. Remote sampling by cameras or hair snags may reduce study costs and duration, yet without telemetry data inflated density estimates remain problematic. 2. We evaluated recently developed spatially explicit capture–recapture (SECR) models using data from a common large carnivore, the American black bear Ursus americanus, obtained by remote sampling of 11 geographically open populations. These models permit direct estimation of population density from C–R data without assuming geographic closure. We compared estimates derived using this approach to those derived using conventional approaches that estimate density as /. 3. Spatially explicit C–R estimates were 20–200% lower than densities estimated as /. AICc supported individual heterogeneity in capture probabilities and home range sizes. Variable home range size could not be accounted for when estimating density as /. 4. Synthesis and applications. We conclude that the higher densities estimated as / compared to estimates from SECR models are consistent with positive bias due to edge effects in the former. Inflated density estimates could lead to management decisions placing threatened or endangered large carnivores at greater risk. Such decisions could be avoided by estimating density by SECR when bias due to geographic closure violation cannot be minimized by study design.